CN108649557A - It is a kind of to consider that dual probabilistic electric power system source lotus coordinates restoration methods - Google Patents
It is a kind of to consider that dual probabilistic electric power system source lotus coordinates restoration methods Download PDFInfo
- Publication number
- CN108649557A CN108649557A CN201810338058.5A CN201810338058A CN108649557A CN 108649557 A CN108649557 A CN 108649557A CN 201810338058 A CN201810338058 A CN 201810338058A CN 108649557 A CN108649557 A CN 108649557A
- Authority
- CN
- China
- Prior art keywords
- unit
- formula
- launched
- load
- uncertainty
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
Abstract
Considering that dual probabilistic electric power system source lotus coordinates restoration methods the invention discloses a kind of, includes the following steps：S1：Dual analysis of uncertainty：The dual uncertainty for analyzing unit starting time to be launched and the practical amount of recovery of load establishes the symbol recovery characteristic of black starting-up unit, unit to be launched and load；S2：Consider that dual probabilistic source lotus coordinates restoration methods modeling：Fluctuation range by analyzing Uncertainty carries out mathematical character, the multiple target uncertain optimization model for being up to target with unit output restoration and load weighting amount of recovery is established, the maximum active constraint of unit starting power, single input load, System Reactive Power and unit self-excitation, system operation and maximum critical thermal starting time-constrain are considered；S3：Source lotus based on IGDT coordinates Restoration model and solves：When converting not less than minimum predetermined target ambiguous model to using IGDT, the Robust Optimization Model of Uncertainty fluctuation range is maximized, and is further solved by NSGA II algorithms.
Description
Technical field
The present invention relates to Power system fault restorations, considering dual probabilistic electric power system source more particularly to a kind of
Lotus coordinates restoration methods.
Background technology
As electric system continues to develop, the operating status of system is increasingly complex, due to certain accidental and necessary factor
In the presence of large-scale blackout seriously threatens as what current power system must face.And during black starting-up, to ensure unit
Stable operation should reach minimum stablize and contribute, it is therefore necessary to which recovered part load goes out for balancing unit as early as possible after grid-connected
Power.
Secondly, the dual uncertainty of unit recovery time and load restoration amount has a significant impact to recovery policy.
The distribution characteristics of Uncertainty and fuzzy parameter are difficult to obtain in recovery process, can not fundamentally and solve uncertainty ask
Topic.
Invention content
Goal of the invention：The considerations of capable of solving defect existing in the prior art the object of the present invention is to provide one kind, is dual
Probabilistic electric power system source lotus coordinates restoration methods.
Technical solution：To reach this purpose, the present invention uses following technical scheme：
The dual probabilistic electric power system source lotus of consideration of the present invention coordinates restoration methods, includes the following steps：
S1：Dual analysis of uncertainty：Analyze unit starting time to be launched and the practical amount of recovery of load it is dual not really
It is qualitative, establish the symbol recovery characteristic of black starting-up unit, unit to be launched and load；
S2：Consider that dual probabilistic source lotus coordinates restoration methods modeling：By the fluctuation range for analyzing Uncertainty
Mathematical character is carried out, the multiple target uncertain optimization mould for being up to target with unit output restoration and load weighting amount of recovery is established
Type considers the maximum active constraint of unit starting power, single input load, System Reactive Power and unit self-excitation, system and transports
Row and maximum critical thermal starting time-constrain；
S3：Source lotus based on IGDT coordinates Restoration model and solves：Ambiguous model is converted not less than most to using IGDT
When low goal-selling, the Robust Optimization Model of Uncertainty fluctuation range is maximized, and is further asked by NSGA-II algorithms
Solution.
Further, the step S1 includes the following steps：
S1.1：Unit starting time uncertainty analysis to be launched：As shown in formula (1)；
In formula (1),For the Uncertainty of j-th of set grid-connection time to be launched,It is opened for j-th of unit to be launched
The Uncertainty of dynamic Period Length, tEsujEmpirically to start the time with the prediction of j-th of unit to be launched of historical data；
a1For UncertaintyAround tEsujFluctuating range, i.e., j-th unit practical startup time to be launched is in (1-a1, 1+a1) model
It is fluctuated up and down around predicted value in enclosing；tNsjAt the time of to start j-th of unit to be launched；
S1.2：Load restoration amount analysis of uncertainty：As shown in formula (2)；
In formula (2), PlkFor the amount of recovery of first of load bus kth outlet,For PlkUncertainty, PElkFor l
The prediction load restoration amount of a load bus kth outlet；a2For UncertaintyAround PElkFluctuating range, i.e., it is practical extensive
Multiple load is in (1-a2, 1+a2) the interior fluctuation above and below predicted value of range；
S1.3：Black starting-up unit symbol recovery specificity analysis：As shown in formula (3)；
In formula (3), PBSUi(t) be i-th of black starting-up unit in the force function that goes out of t moment, KBiIt is equivalent for black starting-up unit
Creep speed, PmaxBiFor the maximum output of i-th of black starting-up unit injected system；
S1.4：Unit symbol recovery specificity analysis to be launched：As shown in formula (4)；
In formula (4), PNBSUj(t) be j-th of unit to be launched in the force function that goes out of t moment, KNjFor j-th of machine to be launched
The equivalent creep speed of group, PmaxNjFor the maximum output of j-th of unit injected system to be launched, PNstjFor j-th of machine to be launched
The station-service electrical power of group；
S1.5：Load symbol recovery specificity analysis：As shown in formula (5)；
In formula (5), PLoadlk(t) it is the load restoration amount of t moment, tLclkThe time restored for load input.
Further, the step S2 includes the following steps：
S2.1：Objective function：
Maximize unit output f1, i.e.,
In formula (6), n is black starting-up unit quantity, and m is unit quantity to be launched, teRestore total time for the system of setting,
PBSUi(t) be i-th of black starting-up unit in the force function that goes out of t moment, PNBSUj(t) it is j-th of unit to be launched going out in t moment
Force function；
While unit restores, the input of important load is considered to balance unit output, realizes that the coordination of source lotus restores,
As shown in formula (7)：
In formula (7), NkFor load quantity to be restored, NpFor the line number that goes out on first of load bus, ωlkFor first of load
The significance level of node kth outlet, PLoadlk(t) it is the load restoration amount of t moment；
It obtains shown in multiple target uncertain optimization model such as formula (8)：
Max(f1,f2) (8)
S2.2：Define constraints：
The startup power of j-th of unit to be launched is constrained as shown in formula (9)：
Formula (9) is formed by 4, and the 1st is black starting-up unit output, and the 2nd is the unit output to be launched having been turned on, the
3 are load restoration amount, and the 4th is j-th of required startup power of unit starting to be launched；uj(t) it indicates to wait opening for j-th
Motivation group only works as u in the state of moment tjAnd u (t)=0j(when t+ Δs t)=1, show j-th of unit to be launched in moment t
It starts；Δ t is incremental time, PNstjFor the station-service electrical power of j-th of unit to be launched；
Single puts into the maximum active constraint of load as shown in formula (10)：
In formula (10), PlkFor the amount of recovery of first of load bus kth outlet,For PlkUncertainty, PBSUniFor
The specified active power output of i-th of black starting-up unit；PNBSUnjFor the specified active power output of j-th of unit to be launched；ΔfmaxFor frequency
The maximum allowable drop-out value of rate；fdiFor the frequency response values of i-th of black starting-up unit, fdjFor the frequency of j-th of unit to be launched
Response；
Idle constraint is as shown in formula (11)：
In formula (11), NpFor the number of, lines in restoration path；QpFor the charging reactive power of pth circuit；QBimaxFor
I-th of absorbent maximum reactive power of black starting-up unit institute；
Black starting-up unit is encouraged oneself shown in magnetic confinement such as formula (12)：
In formula (12), KQiFor the short-circuit ratio of i-th of black starting-up unit, SBiFor the rated power of i-th of black starting-up unit；
System operation is constrained as shown in formula (13)：
In formula (13), QGiFor the idle output of i-th of black starting-up unit, QGjFor j-th unit to be launched it is idle go out
Power；QGiminFor the idle output lower limit of i-th of black starting-up unit, QGimaxFor the idle output upper limit of i-th of black starting-up unit,
QGjminFor the idle output lower limit of j-th of unit to be launched, QGjmaxFor the idle output upper limit of j-th of unit to be launched；ViFor
The voltage of i-th of black starting-up unit, VjFor the voltage of j-th of unit to be launched, VlFor the voltage of load bus；ViminIt is i-th
The set end voltage lower limit of black starting-up unit, VimaxFor the set end voltage upper limit of i-th of black starting-up unit, VjminIt is to be launched for j-th
The set end voltage lower limit of unit, VjmaxFor the upper limit of the set end voltage of j-th of unit to be launched；VlminFor first load bus
Lower voltage limit, VlmaxFor the upper voltage limit of first of load bus；
Shown in the thermal starting time-constrain such as formula (14) of unit：
0 ＜ tNsj＜ tCH (14)
In formula (14), tNsjAt the time of to start j-th of unit to be launched, tCHFor the thermal starting time limit of unit.
Further, in the step S2.1, formula (6) simplifies operation by formula (15)：
In formula (15),For the Uncertainty of j-th of unit starting Period Length to be launched, tNsjIt is waited for start j-th
At the time of starting unit, KNjFor the equivalent creep speed of j-th of unit to be launched.
Further, the step S3 includes the following steps：
S3.1：Calculate minimum load BcWith minimum restoring amount Bd, as shown in formula (16) and (17)：
Bc=(1- δ1)B1 (16)
Bd=(1- δ2)B2 (17)
In formula (16), B1It is formula (15) and formula (7) in tEsujAnd PElkLower progress multiple-objection optimization obtains the total of anticipation scheme
It contributes, B2It is formula (15) and formula (7) in tEsujAnd PElkLower progress multiple-objection optimization obtains the load restoration amount of anticipation scheme；tEsuj
Empirically to start time, P with the prediction of j-th of unit to be launched of historical dataElkGo out for first of load bus kth item
The prediction load restoration amount of line；
S3.2：Robust Optimization Model is obtained, as shown in formula (18)：
In formula (18), a1For UncertaintyAround tEsujFluctuating range, a2For UncertaintyAround PElkFluctuation
Amplitude, tLclkThe time restored for load input；
S3.3：Formula (18) institute representation model is further solved by NSGA-II algorithms.
Advantageous effect：Invention has uncertainty in view of load restoration amount and unit starting time, it is difficult to
Accurate Expression restores the unit under the conditions of certainty and Coordinated Restoration model is improved, passes through source lotus time domain specification
Coupling realizes that timesharing step is intersected and coordinates to restore；The present invention considers system and contributes and load restoration benefit and security constraint
Etc. conditions, it is proposed that it is a kind of to consider that dual probabilistic electric power system source lotus coordinates restoration methods, utilize II algorithms pair of NSGA-
Problem is solved.The unit recovery time and load restoration sequence that the method for the present invention obtains can guarantee that Uncertainty fluctuates situation
Under meet system security constraint, amount of recovery reaches the minimum target of dispatcher's setting, effectively increases the robust of system recovery
Property.
Description of the drawings
Fig. 1 is the flow chart of method in the specific embodiment of the invention；
Fig. 2 is 10 machine of New England, 39 node system figure in the specific embodiment of the invention.
Specific implementation mode
Technical scheme of the present invention is further introduced With reference to embodiment.
Present embodiment discloses a kind of dual probabilistic electric power system source lotus coordination restoration methods of consideration, such as
Shown in Fig. 1, include the following steps：
S1：Dual analysis of uncertainty：Analyze unit starting time to be launched and the practical amount of recovery of load it is dual not really
It is qualitative, establish the symbol recovery characteristic of black starting-up unit, unit to be launched and load；
S2：Consider that dual probabilistic source lotus coordinates restoration methods modeling：By the fluctuation range for analyzing Uncertainty
Mathematical character is carried out, the multiple target uncertain optimization mould for being up to target with unit output restoration and load weighting amount of recovery is established
Type considers the maximum active constraint of unit starting power, single input load, System Reactive Power and unit self-excitation, system and transports
Row and maximum critical thermal starting time-constrain；
S3：Source lotus based on IGDT coordinates Restoration model and solves：Ambiguous model is converted not less than most to using IGDT
When low goal-selling, the Robust Optimization Model of Uncertainty fluctuation range is maximized, and is further asked by NSGA-II algorithms
Solution.
Step S1 includes the following steps：
S1.1：Unit starting time uncertainty analysis to be launched：As shown in formula (1)；
In formula (1),For the Uncertainty of j-th of set grid-connection time to be launched,It is opened for j-th of unit to be launched
The Uncertainty of dynamic Period Length, tEsujEmpirically to start the time with the prediction of j-th of unit to be launched of historical data；
a1For UncertaintyAround tEsujFluctuating range, i.e., j-th unit practical startup time to be launched is in (1-a1, 1+a1) model
It is fluctuated up and down around predicted value in enclosing；tNsjAt the time of to start j-th of unit to be launched；
S1.2：Load restoration amount analysis of uncertainty：As shown in formula (2)；
In formula (2), PlkFor the amount of recovery of first of load bus kth outlet,For PlkUncertainty, PElkFor l
The prediction load restoration amount of a load bus kth outlet；a2For UncertaintyAround PElkFluctuating range, i.e., it is practical extensive
Multiple load is in (1-a2, 1+a2) the interior fluctuation above and below predicted value of range；
S1.3：Black starting-up unit symbol recovery specificity analysis：As shown in formula (3)；
In formula (3), PBSUi(t) be i-th of black starting-up unit in the force function that goes out of t moment, KBiIt is equivalent for black starting-up unit
Creep speed, PmaxBiFor the maximum output of i-th of black starting-up unit injected system；
S1.4：Unit symbol recovery specificity analysis to be launched：As shown in formula (4)；
In formula (4), PNBSUj(t) be j-th of unit to be launched in the force function that goes out of t moment, KNjFor j-th of machine to be launched
The equivalent creep speed of group, PmaxNjFor the maximum output of j-th of unit injected system to be launched, PNstjFor j-th of machine to be launched
The station-service electrical power of group；
S1.5：Load symbol recovery specificity analysis：As shown in formula (5)；
In formula (5), PLoadlk(t) it is the load restoration amount of t moment, tLclkThe time restored for load input.
Step S2 includes the following steps：
S2.1：Objective function：
Maximize unit output f1, i.e.,
In formula (6), n is black starting-up unit quantity, and m is unit quantity to be launched, teRestore total time for the system of setting,
PBSUi(t) be i-th of black starting-up unit in the force function that goes out of t moment, PNBSUj(t) it is j-th of unit to be launched going out in t moment
Force function；
While unit restores, the input of important load is considered to balance unit output, realizes that the coordination of source lotus restores,
As shown in formula (7)：
In formula (7), NkFor load quantity to be restored, NpFor the line number that goes out on first of load bus, ωlkFor first of load
The significance level of node kth outlet, PLoadlk(t) it is the load restoration amount of t moment；
It obtains shown in multiple target uncertain optimization model such as formula (8)：
Max(f1,f2) (8)
S2.2：Define constraints：
The startup power of j-th of unit to be launched is constrained as shown in formula (9)：
Formula (9) is formed by 4, and the 1st is black starting-up unit output, and the 2nd is the unit output to be launched having been turned on, the
3 are load restoration amount, and the 4th is j-th of required startup power of unit starting to be launched；uj(t) it indicates to wait opening for j-th
Motivation group only works as u in the state of moment tjAnd u (t)=0j(when t+ Δs t)=1, show j-th of unit to be launched in moment t
It starts；Δ t is incremental time, PNstjFor the station-service electrical power of j-th of unit to be launched；
Single puts into the maximum active constraint of load as shown in formula (10)：
In formula (10), PlkFor the amount of recovery of first of load bus kth outlet,For PlkUncertainty, PBSUniFor
The specified active power output of i-th of black starting-up unit；PNBSUnjFor the specified active power output of j-th of unit to be launched；ΔfmaxFor frequency
The maximum allowable drop-out value of rate；fdiFor the frequency response values of i-th of black starting-up unit, fdjFor the frequency of j-th of unit to be launched
Response；
Idle constraint is as shown in formula (11)：
In formula (11), NpFor the number of, lines in restoration path；QpFor the charging reactive power of pth circuit；QBimaxFor
I-th of absorbent maximum reactive power of black starting-up unit institute；
Black starting-up unit is encouraged oneself shown in magnetic confinement such as formula (12)：
In formula (12), KQiFor the short-circuit ratio of i-th of black starting-up unit, SBiFor the rated power of i-th of black starting-up unit；
System operation is constrained as shown in formula (13)：
In formula (13), QGiFor the idle output of i-th of black starting-up unit, QGjFor j-th unit to be launched it is idle go out
Power；QGiminFor the idle output lower limit of i-th of black starting-up unit, QGimaxFor the idle output upper limit of i-th of black starting-up unit,
QGjminFor the idle output lower limit of j-th of unit to be launched, QGjmaxFor the idle output upper limit of j-th of unit to be launched；ViFor
The voltage of i-th of black starting-up unit, VjFor the voltage of j-th of unit to be launched, VlFor the voltage of load bus；ViminIt is i-th
The set end voltage lower limit of black starting-up unit, VimaxFor the set end voltage upper limit of i-th of black starting-up unit, VjminIt is to be launched for j-th
The set end voltage lower limit of unit, VjmaxFor the upper limit of the set end voltage of j-th of unit to be launched；VlminFor first load bus
Lower voltage limit, VlmaxFor the upper voltage limit of first of load bus；
Shown in the thermal starting time-constrain such as formula (14) of unit：
0 ＜ tNsj＜ tCH (14)
In formula (14), tNsjAt the time of to start j-th of unit to be launched, tCHFor the thermal starting time limit of unit.
In step S2.1, formula (6) simplifies operation by formula (15)：
In formula (15),For the Uncertainty of j-th of unit starting Period Length to be launched, tNsJ is to start j-th to wait for
At the time of starting unit, KNjFor the equivalent creep speed of j-th of unit to be launched.
Step S3 includes the following steps：
S3.1：Calculate minimum load BcWith minimum restoring amount Bd, as shown in formula (16) and (17)：
Bc=(1- δ1)B1 (16)
Bd=(1- δ2)B2 (17)
In formula (16), B1It is formula (15) and formula (7) in tEsujAnd PElkLower progress multiple-objection optimization obtains the total of anticipation scheme
It contributes, B2It is formula (15) and formula (7) in tEsujAnd PElkLower progress multiple-objection optimization obtains the load restoration amount of anticipation scheme；tEsuj
Empirically to start time, P with the prediction of j-th of unit to be launched of historical dataElkGo out for first of load bus kth item
The prediction load restoration amount of line；
S3.2：Robust Optimization Model is obtained, as shown in formula (18)：
In formula (18), a1For UncertaintyAround tEsujFluctuating range, a2For UncertaintyAround PElkFluctuation
Amplitude, tLclkThe time restored for load input；
S3.3：Formula (18) institute representation model is further solved by NSGA-II algorithms.
It is calculated by taking 10 machine of New England, 39 node system as an example below, as shown in Fig. 2, to prove the feasible of this method
Property.
1 unit parameter of table
Unit parameter is as shown in table 1, wherein No. 33 units are black starting-up unit, remaining is unit to be launched, is not had
Self-startup ability.When not considering unit starting time and load restoration amount uncertainty, it is assumed that the charging time of every section of branch
It it is 2 minutes, when recovery is 1.5 hours a length of.
2 load parameter of table
Load parameter is as shown in table 2, is not considering uncertain factor.Solution obtains unique solution, i.e. unit is maximum
Output restoration 797.25MW, load maximum weighted amount of recovery 343.23, source lotus coordinated Restoration stage at 0-85 minutes.85 minutes with
Afterwards, only remaining sub-load continues to restore.Using the interval of unit commitment time as a time step, the unit solved and negative
The recovery time of lotus and sequence are as shown in table 3.Node serial number where restoring load and X (y) expressions of corresponding outlet serial number, wherein
X is node serial number, and y numbers for outlet.
Table 3 does not consider that source lotus coordinates recovery scheme under uncertain factor
When considering unit recovery time and load restoration amount uncertainty, by changing deviation factors δ1, δ2, determine not
Same expectation target.With δ1It is 0.03, δ2NSGA-II calculating is carried out for being 0.2.Uncertain parameter maximum fluctuation amplitude a1With
a2Bigger, the robustness of practical recovery process is stronger.In order to ensure the balance of two parameters, usually choose in the forward positions Pareto
Point is analyzed, i.e., (0.24,0.13), the recovery time of unit and load and sequence are as shown in table 4.
Table 4 considers that the source lotus under condition of uncertainty coordinates recovery scheme
As seen from the above table, system initial stages of restoration puts into 257.27MW loads and is balanced to system power altogether, and it is total to account for system
8% to contribute.
Claims (5)
1. a kind of considering that dual probabilistic electric power system source lotus coordinates restoration methods, it is characterised in that：Include the following steps：
S1：Dual analysis of uncertainty：The dual uncertainty of unit starting time to be launched and the practical amount of recovery of load are analyzed,
Establish the symbol recovery characteristic of black starting-up unit, unit to be launched and load；
S2：Consider that dual probabilistic source lotus coordinates restoration methods modeling：Fluctuation range by analyzing Uncertainty carries out
Mathematical character establishes the multiple target uncertain optimization model for being up to target with unit output restoration and load weighting amount of recovery,
Consider the maximum active constraint of unit starting power, single input load, System Reactive Power and unit self-excitation, system operation and
Maximum critical thermal starting time-constrain；
S3：Source lotus based on IGDT coordinates Restoration model and solves：It is converted ambiguous model to using IGDT pre- not less than minimum
If when target, maximizing the Robust Optimization Model of Uncertainty fluctuation range, and further solved by NSGA-II algorithms.
2. according to claim 1 consider that dual probabilistic electric power system source lotus coordinates restoration methods, feature exists
In：The step S1 includes the following steps：
S1.1：Unit starting time uncertainty analysis to be launched：As shown in formula (1)；
In formula (1),For the Uncertainty of j-th of set grid-connection time to be launched,For j-th of unit starting to be launched when
The Uncertainty of segment length, tEsujEmpirically to start the time with the prediction of j-th of unit to be launched of historical data；a1For
UncertaintyAround tEsujFluctuating range, i.e., j-th unit practical startup time to be launched is in (1-a1, 1+a1) in range
It is fluctuated up and down around predicted value；tNsjAt the time of to start j-th of unit to be launched；
S1.2：Load restoration amount analysis of uncertainty：As shown in formula (2)；
In formula (2), PlkFor the amount of recovery of first of load bus kth outlet,For PlkUncertainty, PElkIt is negative for first
The prediction load restoration amount of lotus node kth outlet；a2For UncertaintyAround PElkFluctuating range, i.e., actually restore
Load is in (1-a2, 1+a2) the interior fluctuation above and below predicted value of range；
S1.3：Black starting-up unit symbol recovery specificity analysis：As shown in formula (3)；
In formula (3), PBSUi(t) be i-th of black starting-up unit in the force function that goes out of t moment, KBiFor the equivalent climbing of black starting-up unit
Rate, PmaxBiFor the maximum output of i-th of black starting-up unit injected system；
S1.4：Unit symbol recovery specificity analysis to be launched：As shown in formula (4)；
In formula (4), PNBSUj(t) be j-th of unit to be launched in the force function that goes out of t moment, KNjFor j-th unit to be launched etc.
Imitate creep speed, PmaxNjFor the maximum output of j-th of unit injected system to be launched, PNstjFor the factory of j-th of unit to be launched
Electric power；
S1.5：Load symbol recovery specificity analysis：As shown in formula (5)；
In formula (5), PLoadlk(t) it is the load restoration amount of t moment, tLclkThe time restored for load input.
3. according to claim 1 consider that dual probabilistic electric power system source lotus coordinates restoration methods, feature exists
In：The step S2 includes the following steps：
S2.1：Objective function：
Maximize unit output f1, i.e.,
In formula (6), n is black starting-up unit quantity, and m is unit quantity to be launched, teRestore total time, P for the system of settingBSUi
(t) be i-th of black starting-up unit in the force function that goes out of t moment, PNBSUj(t) it is output letter of j-th of unit to be launched in t moment
Number；
While unit restores, the input of important load is considered to balance unit output, realizes that the coordination of source lotus restores, such as formula
(7) shown in：
In formula (7), NkFor load quantity to be restored, NpFor the line number that goes out on first of load bus, ωlkFor first of load bus
The significance level of kth outlet, PLoadlk(t) it is the load restoration amount of t moment；
It obtains shown in multiple target uncertain optimization model such as formula (8)：
Max(f1,f2) (8)
S2.2：Define constraints：
The startup power of j-th of unit to be launched is constrained as shown in formula (9)：
Formula (9) is formed by 4, and the 1st is black starting-up unit output, and the 2nd is the unit output to be launched having been turned on, the 3rd
For load restoration amount, the 4th is j-th of required startup power of unit starting to be launched；uj(t) indicate j-th it is to be launched
Unit only works as u in the state of moment tjAnd u (t)=0j(when t+ Δs t)=1, show that j-th of unit to be launched is opened in moment t
It moves；Δ t is incremental time, PNstjFor the station-service electrical power of j-th of unit to be launched；
Single puts into the maximum active constraint of load as shown in formula (10)：
In formula (10), PlkFor the amount of recovery of first of load bus kth outlet,For PlkUncertainty, PBSUniIt is i-th
The specified active power output of black starting-up unit；PNBSUnjFor the specified active power output of j-th of unit to be launched；ΔfmaxFor frequency maximum
The drop-out value of permission；fdiFor the frequency response values of i-th of black starting-up unit, fdjFor the frequency response of j-th of unit to be launched
Value；
Idle constraint is as shown in formula (11)：
In formula (11), NpFor the number of, lines in restoration path；QpFor the charging reactive power of pth circuit；QBimaxIt is i-th
The absorbent maximum reactive power of black starting-up unit institute；
Black starting-up unit is encouraged oneself shown in magnetic confinement such as formula (12)：
In formula (12), KQiFor the short-circuit ratio of i-th of black starting-up unit, SBiFor the rated power of i-th of black starting-up unit；
System operation is constrained as shown in formula (13)：
In formula (13), QGiFor the idle output of i-th of black starting-up unit, QGjFor the idle output of j-th of unit to be launched；
QGiminFor the idle output lower limit of i-th of black starting-up unit, QGimaxFor the idle output upper limit of i-th of black starting-up unit, QGjmin
For the idle output lower limit of j-th of unit to be launched, QGjmaxFor the idle output upper limit of j-th of unit to be launched；ViIt is i-th
The voltage of black starting-up unit, VjFor the voltage of j-th of unit to be launched, VlFor the voltage of load bus；ViminIt black is opened for i-th
The set end voltage lower limit of motivation group, VimaxFor the set end voltage upper limit of i-th of black starting-up unit, VjminFor j-th of unit to be launched
Set end voltage lower limit, VjmaxFor the upper limit of the set end voltage of j-th of unit to be launched；VlminFor the voltage of first of load bus
Lower limit, VlmaxFor the upper voltage limit of first of load bus；
Shown in the thermal starting time-constrain such as formula (14) of unit：
0 ＜ tNsj＜ tCH (14)
In formula (14), tNsjAt the time of to start j-th of unit to be launched, tCHFor the thermal starting time limit of unit.
4. according to claim 3 consider that dual probabilistic electric power system source lotus coordinates restoration methods, feature exists
In：In the step S2.1, formula (6) simplifies operation by formula (15)：
In formula (15),For the Uncertainty of j-th of unit starting Period Length to be launched, tNsjTo start j-th of machine to be launched
At the time of group, KNjFor the equivalent creep speed of j-th of unit to be launched.
5. according to claim 4 consider that dual probabilistic electric power system source lotus coordinates restoration methods, feature exists
In：The step S3 includes the following steps：
S3.1：Calculate minimum load BcWith minimum restoring amount Bd, as shown in formula (16) and (17)：
Bc=(1- δ1)B1 (16)
Bd=(1- δ2)B2 (17)
In formula (16), B1It is formula (15) and formula (7) in tEsujAnd PElkLower progress multiple-objection optimization obtains the gross capability of anticipation scheme,
B2It is formula (15) and formula (7) in tEsujAnd PElkLower progress multiple-objection optimization obtains the load restoration amount of anticipation scheme；tEsujFor according to
Prediction according to j-th of unit to be launched of experience and historical data starts time, PElkFor first of load bus kth outlet
Predict load restoration amount；
S3.2：Robust Optimization Model is obtained, as shown in formula (18)：
In formula (18), a1For UncertaintyAround tEsujFluctuating range, a2For UncertaintyAround PElkFluctuation width
Degree, tLclkThe time restored for load input；
S3.3：Formula (18) institute representation model is further solved by NSGA-II algorithms.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810338058.5A CN108649557B (en) | 2018-04-16 | 2018-04-16 | Power system source load coordination recovery method considering double uncertainties |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810338058.5A CN108649557B (en) | 2018-04-16 | 2018-04-16 | Power system source load coordination recovery method considering double uncertainties |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108649557A true CN108649557A (en) | 2018-10-12 |
| CN108649557B CN108649557B (en) | 2020-02-18 |
Family
ID=63746439
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810338058.5A Active CN108649557B (en) | 2018-04-16 | 2018-04-16 | Power system source load coordination recovery method considering double uncertainties |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108649557B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110165666A (en) * | 2019-05-29 | 2019-08-23 | 四川大学 | A kind of active distribution network dispatching method based on IGDT |
| CN111082451A (en) * | 2019-09-18 | 2020-04-28 | 中国电建集团青海省电力设计院有限公司 | Incremental distribution network multi-objective optimization scheduling model based on scene method |
| CN113078633A (en) * | 2021-03-22 | 2021-07-06 | 清华大学深圳国际研究生院 | Method for improving restoring force of power transmission and distribution coupling system containing renewable energy |
| CN113644653A (en) * | 2021-08-20 | 2021-11-12 | 西安交通大学 | New energy and energy storage cooperative power system black start path recovery method |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040243350A1 (en) * | 2003-05-22 | 2004-12-02 | Smith Raub Warfield | Methods of measuring steam turbine efficiency |
| US20050182576A1 (en) * | 2003-05-22 | 2005-08-18 | General Electric Company | Methods of measuring steam turbine efficiency |
| CN104463375A (en) * | 2014-12-24 | 2015-03-25 | 贵州电网公司电力调度控制中心 | Power grid disaster recovery control model modeling method based on CIM standard |
| CN106992519A (en) * | 2017-05-12 | 2017-07-28 | 南京理工大学 | A kind of network load based on information gap decision theory recovers robust Optimal methods |
| CN107204631A (en) * | 2017-06-20 | 2017-09-26 | 广东电网有限责任公司电力调度控制中心 | A kind of power network quick recovery method for considering generating set recovery time model |
| CN107862405A (en) * | 2017-10-27 | 2018-03-30 | 广东电网有限责任公司电力调度控制中心 | The power system rack reconstruction and optimization method of meter and microgrid as black starting-up power supply |
-
2018
- 2018-04-16 CN CN201810338058.5A patent/CN108649557B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040243350A1 (en) * | 2003-05-22 | 2004-12-02 | Smith Raub Warfield | Methods of measuring steam turbine efficiency |
| US20050182576A1 (en) * | 2003-05-22 | 2005-08-18 | General Electric Company | Methods of measuring steam turbine efficiency |
| CN104463375A (en) * | 2014-12-24 | 2015-03-25 | 贵州电网公司电力调度控制中心 | Power grid disaster recovery control model modeling method based on CIM standard |
| CN106992519A (en) * | 2017-05-12 | 2017-07-28 | 南京理工大学 | A kind of network load based on information gap decision theory recovers robust Optimal methods |
| CN107204631A (en) * | 2017-06-20 | 2017-09-26 | 广东电网有限责任公司电力调度控制中心 | A kind of power network quick recovery method for considering generating set recovery time model |
| CN107862405A (en) * | 2017-10-27 | 2018-03-30 | 广东电网有限责任公司电力调度控制中心 | The power system rack reconstruction and optimization method of meter and microgrid as black starting-up power supply |
Non-Patent Citations (1)
| Title |
|---|
| ANYA CASTILLO ET AL.: "Microgrid Provision of Blackstart in Disaster Recovery for Power System Restoration", 《IEEE SMARTGRIDCOMM 2013 SYMPOSIUM - SMART GRID SERVICES AND MANAGEMENT MODELS》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110165666A (en) * | 2019-05-29 | 2019-08-23 | 四川大学 | A kind of active distribution network dispatching method based on IGDT |
| CN110165666B (en) * | 2019-05-29 | 2021-08-27 | 四川大学 | Active power distribution network scheduling method based on IGDT |
| CN111082451A (en) * | 2019-09-18 | 2020-04-28 | 中国电建集团青海省电力设计院有限公司 | Incremental distribution network multi-objective optimization scheduling model based on scene method |
| CN111082451B (en) * | 2019-09-18 | 2023-04-18 | 中国电建集团青海省电力设计院有限公司 | Incremental distribution network multi-objective optimization scheduling model based on scene method |
| CN113078633A (en) * | 2021-03-22 | 2021-07-06 | 清华大学深圳国际研究生院 | Method for improving restoring force of power transmission and distribution coupling system containing renewable energy |
| CN113644653A (en) * | 2021-08-20 | 2021-11-12 | 西安交通大学 | New energy and energy storage cooperative power system black start path recovery method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108649557B (en) | 2020-02-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108649557A (en) | It is a kind of to consider that dual probabilistic electric power system source lotus coordinates restoration methods | |
| CN110147907B (en) | Method for constructing virtual power plant double-layer optimization model considering network constraints | |
| Liu et al. | Multi‐objective restoration optimisation of power systems with battery energy storage systems | |
| CN110581571A (en) | dynamic optimization scheduling method for active power distribution network | |
| CN112183855B (en) | Regional power spot market clearing method and device and electronic equipment | |
| Ghaderi Darebaghi et al. | Dynamic multi‐stage under frequency load shedding considering uncertainty of generation loss | |
| Sun et al. | Reliability assessment of cyber-physical distribution network based on the fault tree | |
| Xie et al. | Second-order conic programming model for load restoration considering uncertainty of load increment based on information gap decision theory | |
| Sun et al. | Improving the restorability of bulk power systems with the implementation of a WF-BESS system | |
| Zhao et al. | Optimal generator start-up sequence for bulk system restoration with active distribution networks | |
| Wei et al. | Optimal automatic generation controllers in a multi‐area interconnected power system with utility‐scale PV plants | |
| CN110765591B (en) | Distributed state sensing and optimizing method for power distribution network based on block chain technology | |
| Li et al. | Multiple degradation-driven preventive maintenance policy for serial-parallel multi-station manufacturing systems | |
| Huang et al. | Study on online under‐frequency load shedding strategy with virtual inertia control of wind turbines | |
| Sun et al. | Rolling optimization of transmission network recovery and load restoration considering hybrid wind-storage system and cold load pickup | |
| Massaoudi et al. | Navigating the Landscape of Deep Reinforcement Learning for Power System Stability Control: A Review | |
| Usman et al. | A novel tractable methodology to stochastic multi-period AC OPF in active distribution systems using sequential linearization algorithm | |
| Ding et al. | Electricity market clearing price forecast based on adaptive Kalman filter | |
| CN112928780A (en) | Power distribution network post-disaster power supply recovery method and system | |
| Sun et al. | Determining optimal generator start-up sequence in bulk power system restoration considering uncertainties: A confidence gap decision theory based robust optimization approach | |
| Liao et al. | Two-stage optimization method for network reconfiguration and load recovery during power system restoration | |
| Zhang et al. | Day-ahead stochastic optimal dispatch of LCC-HVDC interconnected power system considering flexibility improvement measures of sending system | |
| CN115360768A (en) | Power scheduling method and device based on muzero and deep reinforcement learning and storage medium | |
| CN115021406A (en) | Microgrid controller integrating economic model predictive control | |
| CN113222354B (en) | Centralized spot market clearing method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |